AMS 573, Design and Analysis of Categorical Data
Measuring the strength of association between pairs of categorical variables. Methods for evaluating classification procedures and inter-rater agreement. Analysis of the associations among three or more categorical variables using log linear models. Logistic regression. 
Pre-requisite:  AMS 572
3 credits, ABCF grading 

Texts:
Categorical Data Analysis, by Alan Agresti, 3rd edition, Wiley Publisher, 2013; ISBN: 978-0-470-46363-5 (required)

Categorical Data Analysis Using SAS by Maura E. Stokes, Charles S. Davis, Gary G. Koch, 3rd edition, SAS publisher, 2012; ISBN: 978-1-60764-664-8 (optional/recommended)

Spring Semester

Learning Outcomes:

1) Demonstrate skills of working with various categorical data, including binary, nominal, ordinal and count data:
      * Expectation, variance, covariance and probability density function;
      * Point estimation with maximal likelihood method;
       * Hypothesis testing with Wald, score and likelihood ratio tests;
       * Constructing confidence intervals based on Wald, score and likelihood ratio test statistics.

2) Demonstrate skills with statistical inference for contingency tables (joint distribution of categorical variables):
      * Difference of proportions, relative risk and odds ratio;
      * Chi-squared tests;
      * Fisher’s exact test;
      * McNemar test for matched pairs.

3) Demonstrate skills with statistical modeling for binary/nominal/ordinal response:
      * Build and apply logistic regression, baseline category and cumulative logit models;
      * Maximal likelihood fitting and goodness of fit tests;
      * Model diagnostic and model selection;
      * Other link functions: log-log, complementary log-log.

4) Demonstrate skills with statistical modeling for count data:
      * Build and apply log-linear models;
      * Connection between log-linear and logit models;
      * Model fitting and goodness of fit tests;
      * Association graphs and collapsibility.

5) Demonstrate skills with proficient usage of standard statistical software tools for categorical data analysis:
      * Understanding of the assumptions, derivation and interpretation of results from statistical analysis;
      * Proficient in SAS procedures: FREQ, GENMOD, GLM and LOGISTIC.